几分钟网站做渔网,2018年互联网大会,关闭未备案网站,上海千锋教育DeepLab复现的pytorch实现
本文复现的主要是deeplabv3。使用的数据集和之前发的文章FCN一样#xff0c;没有了解的可以移步到之前发的文章中去查看一下。
1.该模型的主要结构 对于代码部分#xff0c;主要只写了模型部分的#xff0c;其他部分内容基本和FCN的一致#xf…DeepLab复现的pytorch实现
本文复现的主要是deeplabv3。使用的数据集和之前发的文章FCN一样没有了解的可以移步到之前发的文章中去查看一下。
1.该模型的主要结构 对于代码部分主要只写了模型部分的其他部分内容基本和FCN的一致在下面也会给出完整代码仓库的地址方便大家进行学习。
from collections import OrderedDictfrom typing import Dict, Listimport torch
from torch import nn, Tensor
from torch.nn import functional as F
from .resnet_backbone import resnet50, resnet101
from .mobilenet_backbone import mobilenet_v3_largeclass IntermediateLayerGetter(nn.ModuleDict): # 获取模型指定的中间层输出Module wrapper that returns intermediate layers from a modelIt has a strong assumption that the modules have been registeredinto the model in the same order as they are used.This means that one should **not** reuse the same nn.Moduletwice in the forward if you want this to work.Additionally, it is only able to query submodules that are directlyassigned to the model. So if model is passed, model.feature1 canbe returned, but not model.feature1.layer2.Args:model (nn.Module): model on which we will extract the featuresreturn_layers (Dict[name, new_name]): a dict containing the namesof the modules for which the activations will be returned asthe key of the dict, and the value of the dict is the nameof the returned activation (which the user can specify)._version 2__annotations__ {return_layers: Dict[str, str],}def __init__(self, model: nn.Module, return_layers: Dict[str, str]) - None:if not set(return_layers).issubset([name for name, _ in model.named_children()]):raise ValueError(return_layers are not present in model)orig_return_layers return_layersreturn_layers {str(k): str(v) for k, v in return_layers.items()}# 重新构建backbone将没有使用到的模块全部删掉layers OrderedDict()for name, module in model.named_children():layers[name] moduleif name in return_layers:del return_layers[name]if not return_layers:breaksuper(IntermediateLayerGetter, self).__init__(layers)self.return_layers orig_return_layersdef forward(self, x: Tensor) - Dict[str, Tensor]:out OrderedDict()for name, module in self.items():x module(x)if name in self.return_layers:out_name self.return_layers[name]out[out_name] xreturn outclass DeepLabV3(nn.Module):Implements DeepLabV3 model fromRethinking Atrous Convolution for Semantic Image Segmentationhttps://arxiv.org/abs/1706.05587_.Args:backbone (nn.Module): the network used to compute the features for the model.The backbone should return an OrderedDict[Tensor], with the key beingout for the last feature map used, and aux if an auxiliary classifieris used.classifier (nn.Module): module that takes the out element returned fromthe backbone and returns a dense prediction.aux_classifier (nn.Module, optional): auxiliary classifier used during training__constants__ [aux_classifier]def __init__(self, backbone, classifier, aux_classifierNone):super(DeepLabV3, self).__init__()self.backbone backboneself.classifier classifierself.aux_classifier aux_classifierdef forward(self, x: Tensor) - Dict[str, Tensor]:input_shape x.shape[-2:]# contract: features is a dict of tensorsfeatures self.backbone(x)result OrderedDict()x features[out]x self.classifier(x)# 使用双线性插值还原回原图尺度x F.interpolate(x, sizeinput_shape, modebilinear, align_cornersFalse)result[out] xif self.aux_classifier is not None:x features[aux]x self.aux_classifier(x)# 使用双线性插值还原回原图尺度x F.interpolate(x, sizeinput_shape, modebilinear, align_cornersFalse)result[aux] xreturn resultclass FCNHead(nn.Sequential):def __init__(self, in_channels, channels):inter_channels in_channels // 4 # 两个//表示地板除即先做除法然后向下取整super(FCNHead, self).__init__(nn.Conv2d(in_channels, inter_channels, 3, padding1, biasFalse),nn.BatchNorm2d(inter_channels),nn.ReLU(),nn.Dropout(0.1),nn.Conv2d(inter_channels, channels, 1))class ASPPConv(nn.Sequential):def __init__(self, in_channels: int, out_channels: int, dilation: int) - None:super(ASPPConv, self).__init__(nn.Conv2d(in_channels, out_channels, 3, paddingdilation, dilationdilation, biasFalse),nn.BatchNorm2d(out_channels),nn.ReLU())class ASPPPooling(nn.Sequential):def __init__(self, in_channels: int, out_channels: int) - None:super(ASPPPooling, self).__init__(nn.AdaptiveAvgPool2d(1),nn.Conv2d(in_channels, out_channels, 1, biasFalse),nn.BatchNorm2d(out_channels),nn.ReLU())def forward(self, x: torch.Tensor) - torch.Tensor:size x.shape[-2:]for mod in self:x mod(x)return F.interpolate(x, sizesize, modebilinear, align_cornersFalse)class ASPP(nn.Module):def __init__(self, in_channels: int, atrous_rates: List[int], out_channels: int 256) - None:super(ASPP, self).__init__()modules [nn.Sequential(nn.Conv2d(in_channels, out_channels, 1, biasFalse),nn.BatchNorm2d(out_channels),nn.ReLU())]rates tuple(atrous_rates)for rate in rates:modules.append(ASPPConv(in_channels, out_channels, rate))modules.append(ASPPPooling(in_channels, out_channels))self.convs nn.ModuleList(modules)self.project nn.Sequential(nn.Conv2d(len(self.convs) * out_channels, out_channels, 1, biasFalse),nn.BatchNorm2d(out_channels),nn.ReLU(),nn.Dropout(0.5))def forward(self, x: torch.Tensor) - torch.Tensor:_res []for conv in self.convs:_res.append(conv(x))res torch.cat(_res, dim1)return self.project(res)class DeepLabHead(nn.Sequential):def __init__(self, in_channels: int, num_classes: int) - None:super(DeepLabHead, self).__init__(ASPP(in_channels, [12, 24, 36]),nn.Conv2d(256, 256, 3, padding1, biasFalse),nn.BatchNorm2d(256),nn.ReLU(),nn.Conv2d(256, num_classes, 1))def deeplabv3_resnet50(aux, num_classes21, pretrain_backboneFalse):# resnet50_imagenet: https://download.pytorch.org/models/resnet50-0676ba61.pth# deeplabv3_resnet50_coco: https://download.pytorch.org/models/deeplabv3_resnet50_coco-cd0a2569.pthbackbone resnet50(replace_stride_with_dilation[False, True, True])if pretrain_backbone:# 载入resnet50 backbone预训练权重backbone.load_state_dict(torch.load(resnet50.pth, map_locationcpu))out_inplanes 2048aux_inplanes 1024return_layers {layer4: out}if aux:return_layers[layer3] auxbackbone IntermediateLayerGetter(backbone, return_layersreturn_layers)aux_classifier None# why using aux: https://github.com/pytorch/vision/issues/4292if aux:aux_classifier FCNHead(aux_inplanes, num_classes)classifier DeepLabHead(out_inplanes, num_classes)model DeepLabV3(backbone, classifier, aux_classifier)return modeldef deeplabv3_resnet101(aux, num_classes21, pretrain_backboneFalse):# resnet101_imagenet: https://download.pytorch.org/models/resnet101-63fe2227.pth# deeplabv3_resnet101_coco: https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pthbackbone resnet101(replace_stride_with_dilation[False, True, True])if pretrain_backbone:# 载入resnet101 backbone预训练权重backbone.load_state_dict(torch.load(resnet101.pth, map_locationcpu))out_inplanes 2048aux_inplanes 1024return_layers {layer4: out}if aux:return_layers[layer3] auxbackbone IntermediateLayerGetter(backbone, return_layersreturn_layers)aux_classifier None# why using aux: https://github.com/pytorch/vision/issues/4292if aux:aux_classifier FCNHead(aux_inplanes, num_classes)classifier DeepLabHead(out_inplanes, num_classes)model DeepLabV3(backbone, classifier, aux_classifier)return modeldef deeplabv3_mobilenetv3_large(aux, num_classes21, pretrain_backboneFalse):# mobilenetv3_large_imagenet: https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth# depv3_mobilenetv3_large_coco: https://download.pytorch.org/models/deeplabv3_mobilenet_v3_large-fc3c493d.pthbackbone mobilenet_v3_large(dilatedTrue)if pretrain_backbone:# 载入mobilenetv3 large backbone预训练权重backbone.load_state_dict(torch.load(mobilenet_v3_large.pth, map_locationcpu))backbone backbone.features# Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks.# The first and last blocks are always included because they are the C0 (conv1) and Cn.stage_indices [0] [i for i, b in enumerate(backbone) if getattr(b, is_strided, False)] [len(backbone) - 1]out_pos stage_indices[-1] # use C5 which has output_stride 16out_inplanes backbone[out_pos].out_channelsaux_pos stage_indices[-4] # use C2 here which has output_stride 8aux_inplanes backbone[aux_pos].out_channelsreturn_layers {str(out_pos): out}if aux:return_layers[str(aux_pos)] auxbackbone IntermediateLayerGetter(backbone, return_layersreturn_layers)aux_classifier None# why using aux: https://github.com/pytorch/vision/issues/4292if aux:aux_classifier FCNHead(aux_inplanes, num_classes)classifier DeepLabHead(out_inplanes, num_classes)model DeepLabV3(backbone, classifier, aux_classifier)return model----------------------------------------------------------------------------------分割线-------------------------------------------from typing import Callable, List, Optionalimport torch
from torch import nn, Tensor
from torch.nn import functional as F
from functools import partialdef _make_divisible(ch, divisor8, min_chNone): # 为了使每一层的通道数都可以被8整除This function is taken from the original tf repo.It ensures that all layers have a channel number that is divisible by 8It can be seen here:https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.pyif min_ch is None:min_ch divisornew_ch max(min_ch, int(ch divisor / 2) // divisor * divisor)# Make sure that round down does not go down by more than 10%.if new_ch 0.9 * ch:new_ch divisorreturn new_chclass ConvBNActivation(nn.Sequential):def __init__(self,in_planes: int,out_planes: int,kernel_size: int 3,stride: int 1,groups: int 1,norm_layer: Optional[Callable[..., nn.Module]] None,activation_layer: Optional[Callable[..., nn.Module]] None,dilation: int 1):padding (kernel_size - 1) // 2 * dilationif norm_layer is None:norm_layer nn.BatchNorm2dif activation_layer is None:activation_layer nn.ReLU6super(ConvBNActivation, self).__init__(nn.Conv2d(in_channelsin_planes,out_channelsout_planes,kernel_sizekernel_size,stridestride,dilationdilation,paddingpadding,groupsgroups,biasFalse),norm_layer(out_planes),activation_layer(inplaceTrue))self.out_channels out_planesclass SqueezeExcitation(nn.Module):def __init__(self, input_c: int, squeeze_factor: int 4):super(SqueezeExcitation, self).__init__()squeeze_c _make_divisible(input_c // squeeze_factor, 8)self.fc1 nn.Conv2d(input_c, squeeze_c, 1)self.fc2 nn.Conv2d(squeeze_c, input_c, 1)def forward(self, x: Tensor) - Tensor:scale F.adaptive_avg_pool2d(x, output_size(1, 1))scale self.fc1(scale)scale F.relu(scale, inplaceTrue)scale self.fc2(scale)scale F.hardsigmoid(scale, inplaceTrue)return scale * xclass InvertedResidualConfig:def __init__(self,input_c: int,kernel: int,expanded_c: int,out_c: int,use_se: bool,activation: str,stride: int,dilation: int,width_multi: float):self.input_c self.adjust_channels(input_c, width_multi)self.kernel kernelself.expanded_c self.adjust_channels(expanded_c, width_multi)self.out_c self.adjust_channels(out_c, width_multi)self.use_se use_seself.use_hs activation HS # whether using h-swish activationself.stride strideself.dilation dilationstaticmethoddef adjust_channels(channels: int, width_multi: float):return _make_divisible(channels * width_multi, 8)class InvertedResidual(nn.Module):def __init__(self,cnf: InvertedResidualConfig,norm_layer: Callable[..., nn.Module]):super(InvertedResidual, self).__init__()if cnf.stride not in [1, 2]:raise ValueError(illegal stride value.)self.use_res_connect (cnf.stride 1 and cnf.input_c cnf.out_c)layers: List[nn.Module] []activation_layer nn.Hardswish if cnf.use_hs else nn.ReLU# expandif cnf.expanded_c ! cnf.input_c:layers.append(ConvBNActivation(cnf.input_c,cnf.expanded_c,kernel_size1,norm_layernorm_layer,activation_layeractivation_layer))# depthwisestride 1 if cnf.dilation 1 else cnf.stridelayers.append(ConvBNActivation(cnf.expanded_c,cnf.expanded_c,kernel_sizecnf.kernel,stridestride,dilationcnf.dilation,groupscnf.expanded_c,norm_layernorm_layer,activation_layeractivation_layer))if cnf.use_se:layers.append(SqueezeExcitation(cnf.expanded_c))# projectlayers.append(ConvBNActivation(cnf.expanded_c,cnf.out_c,kernel_size1,norm_layernorm_layer,activation_layernn.Identity))self.block nn.Sequential(*layers)self.out_channels cnf.out_cself.is_strided cnf.stride 1def forward(self, x: Tensor) - Tensor:result self.block(x)if self.use_res_connect:result xreturn resultclass MobileNetV3(nn.Module):def __init__(self,inverted_residual_setting: List[InvertedResidualConfig],last_channel: int,num_classes: int 1000,block: Optional[Callable[..., nn.Module]] None,norm_layer: Optional[Callable[..., nn.Module]] None):super(MobileNetV3, self).__init__()if not inverted_residual_setting:raise ValueError(The inverted_residual_setting should not be empty.)elif not (isinstance(inverted_residual_setting, List) andall([isinstance(s, InvertedResidualConfig) for s in inverted_residual_setting])):raise TypeError(The inverted_residual_setting should be List[InvertedResidualConfig])if block is None:block InvertedResidualif norm_layer is None:norm_layer partial(nn.BatchNorm2d, eps0.001, momentum0.01)layers: List[nn.Module] []# building first layerfirstconv_output_c inverted_residual_setting[0].input_clayers.append(ConvBNActivation(3,firstconv_output_c,kernel_size3,stride2,norm_layernorm_layer,activation_layernn.Hardswish))# building inverted residual blocksfor cnf in inverted_residual_setting:layers.append(block(cnf, norm_layer))# building last several layerslastconv_input_c inverted_residual_setting[-1].out_clastconv_output_c 6 * lastconv_input_clayers.append(ConvBNActivation(lastconv_input_c,lastconv_output_c,kernel_size1,norm_layernorm_layer,activation_layernn.Hardswish))self.features nn.Sequential(*layers)self.avgpool nn.AdaptiveAvgPool2d(1)self.classifier nn.Sequential(nn.Linear(lastconv_output_c, last_channel),nn.Hardswish(inplaceTrue),nn.Dropout(p0.2, inplaceTrue),nn.Linear(last_channel, num_classes))# initial weightsfor m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, modefan_out)if m.bias is not None:nn.init.zeros_(m.bias)elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):nn.init.ones_(m.weight)nn.init.zeros_(m.bias)elif isinstance(m, nn.Linear):nn.init.normal_(m.weight, 0, 0.01)nn.init.zeros_(m.bias)def _forward_impl(self, x: Tensor) - Tensor:x self.features(x)x self.avgpool(x)x torch.flatten(x, 1)x self.classifier(x)return xdef forward(self, x: Tensor) - Tensor:return self._forward_impl(x)def mobilenet_v3_large(num_classes: int 1000,reduced_tail: bool False,dilated: bool False) - MobileNetV3:Constructs a large MobileNetV3 architecture fromSearching for MobileNetV3 https://arxiv.org/abs/1905.02244.weights_link:https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pthArgs:num_classes (int): number of classesreduced_tail (bool): If True, reduces the channel counts of all feature layersbetween C4 and C5 by 2. It is used to reduce the channel redundancy in thebackbone for Detection and Segmentation.dilated: whether using dilated convwidth_multi 1.0bneck_conf partial(InvertedResidualConfig, width_multiwidth_multi)adjust_channels partial(InvertedResidualConfig.adjust_channels, width_multiwidth_multi)reduce_divider 2 if reduced_tail else 1dilation 2 if dilated else 1inverted_residual_setting [# input_c, kernel, expanded_c, out_c, use_se, activation, stride, dilationbneck_conf(16, 3, 16, 16, False, RE, 1, 1),bneck_conf(16, 3, 64, 24, False, RE, 2, 1), # C1bneck_conf(24, 3, 72, 24, False, RE, 1, 1),bneck_conf(24, 5, 72, 40, True, RE, 2, 1), # C2bneck_conf(40, 5, 120, 40, True, RE, 1, 1),bneck_conf(40, 5, 120, 40, True, RE, 1, 1),bneck_conf(40, 3, 240, 80, False, HS, 2, 1), # C3bneck_conf(80, 3, 200, 80, False, HS, 1, 1),bneck_conf(80, 3, 184, 80, False, HS, 1, 1),bneck_conf(80, 3, 184, 80, False, HS, 1, 1),bneck_conf(80, 3, 480, 112, True, HS, 1, 1),bneck_conf(112, 3, 672, 112, True, HS, 1, 1),bneck_conf(112, 5, 672, 160 // reduce_divider, True, HS, 2, dilation), # C4bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, HS, 1, dilation),bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, HS, 1, dilation),]last_channel adjust_channels(1280 // reduce_divider) # C5return MobileNetV3(inverted_residual_settinginverted_residual_setting,last_channellast_channel,num_classesnum_classes)def mobilenet_v3_small(num_classes: int 1000,reduced_tail: bool False,dilated: bool False) - MobileNetV3:Constructs a large MobileNetV3 architecture fromSearching for MobileNetV3 https://arxiv.org/abs/1905.02244.weights_link:https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pthArgs:num_classes (int): number of classesreduced_tail (bool): If True, reduces the channel counts of all feature layersbetween C4 and C5 by 2. It is used to reduce the channel redundancy in thebackbone for Detection and Segmentation.dilated: whether using dilated convwidth_multi 1.0bneck_conf partial(InvertedResidualConfig, width_multiwidth_multi)adjust_channels partial(InvertedResidualConfig.adjust_channels, width_multiwidth_multi)reduce_divider 2 if reduced_tail else 1dilation 2 if dilated else 1inverted_residual_setting [# input_c, kernel, expanded_c, out_c, use_se, activation, stride, dilationbneck_conf(16, 3, 16, 16, True, RE, 2, 1), # C1bneck_conf(16, 3, 72, 24, False, RE, 2, 1), # C2bneck_conf(24, 3, 88, 24, False, RE, 1, 1),bneck_conf(24, 5, 96, 40, True, HS, 2, 1), # C3bneck_conf(40, 5, 240, 40, True, HS, 1, 1),bneck_conf(40, 5, 240, 40, True, HS, 1, 1),bneck_conf(40, 5, 120, 48, True, HS, 1, 1),bneck_conf(48, 5, 144, 48, True, HS, 1, 1),bneck_conf(48, 5, 288, 96 // reduce_divider, True, HS, 2, dilation), # C4bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, HS, 1, dilation),bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, HS, 1, dilation)]last_channel adjust_channels(1024 // reduce_divider) # C5return MobileNetV3(inverted_residual_settinginverted_residual_setting,last_channellast_channel,num_classesnum_classes)
在上述代码中也将之前FCNmodel中没有的mobilenet作为backbone的模型代码也加了上来。
参考链接
288, 96 // reduce_divider, True, “HS”, 2, dilation), # C4 bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, “HS”, 1, dilation), bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, “HS”, 1, dilation) ] last_channel adjust_channels(1024 // reduce_divider) # C5
return MobileNetV3(inverted_residual_settinginverted_residual_setting,last_channellast_channel,num_classesnum_classes)在上述代码中也将之前FCNmodel中没有的mobilenet作为backbone的模型代码也加了上来。参考链接[deep-learning-for-image-processing/pytorch_segmentation/fcn/src/fcn_model.py at bf4384bfc14e295fdbdc967d6b5093cce0bead17 · WZMIAOMIAO/deep-learning-for-image-processing (github.com)](https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/blob/bf4384bfc14e295fdbdc967d6b5093cce0bead17/pytorch_segmentation/fcn/src/fcn_model.py)
